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Plant Disease Detection and Classification Using Hybrid Model Based on Convolutional Auto Encoder and Convolutional Neural Network

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computers, Materials & Continua 2025年第6期83卷 5219-5234页

作者： Tajinder Kumar Sarbjit Kaur Purushottam Sharma Ankita Chhikara Xiaochun Cheng Sachin Lalar Vikram Verma Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaurYamunanagar135133India Department of Computer Science Government PG CollegeAmbala CanttAmbala134003India School of Computer Science&Engineering Galgotias UniversityGreater Noida203201India Department of Computer Science and Applications Kurukshetra UniversityKurukshetra136118India Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Department of CSE Panipat Institute of Engineering and TechnologyPanipat132103India

During its growth stage,the plant is exposed to various *** and early detection of crop diseases is amajor challenge in the horticulture *** infections can harmtotal crop yield and reduce farmers’income if not identified ***’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant *** is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of *** alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without *** automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of *** date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves *** trained on a dataset obtained from the Plant Village *** dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training ***,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.

关键词： Tomato leaf disease deep learning DenseNet-121 convolutional autoencoder convolutional neural network

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Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第5期17卷 71-83页

作者： Aljammal, Ashraf H. taamneh, Salah Qawasmeh, Ahmad Salameh, Hani Bani Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Software Engineering The Hashemite University Zarqa Jordan

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset;the performance of the classifiers has been affected © 2023, International Journal of Interactive Mobile *** Rights Reserved.

关键词： Network security

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Efficient straggler task management in cloud environment using stochastic gradient descent with momentum learning-driven neural networks

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Cluster Computing 2024年第4期27卷 4673-4685页

作者： Swain, Smruti Rekha Parashar, Anshu Singh, Ashutosh Kumar Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Kaohsiung804201 Taiwan

In the modern era, large-scale computing systems distribute tasks into smaller units, allowing them to be executed simultaneously, accelerating job completion, and reducing energy usage. However, cloud computing systems face a significant challenge: the Long Tail problem. This problem arises when a small subset of slow-performing tasks impedes the overall progress of parallel job execution, resulting in longer service response times and decreased system efficiency. To reduce task execution time and energy consumption, we propose an efficient straggler task management framework for cloud data centers in this paper. A neural network-based resource predictor is initially developed and tuned with the Stochastic Gradient Descent with Momentum mechanism to analyze and classify heterogeneous tasks into stragglers and non-stragglers. Then, after identifying the straggler tasks, they are further classified into two categories: Resource Hunters and Long-Tail stragglers, based on their specific resource requirements. A task management policy is implemented to achieve parallelism and enhance sustainability in the cloud infrastructure. Considering the task category, this policy effectively schedules and allocates resources among user job requests. To evaluate the effectiveness of the proposed work, extensive simulations are performed using the Google Cluster Dataset (GCD). The results obtained from these simulations are subsequently compared to state-of-the-art techniques for a comprehensive analysis. The experimental results reveal substantial improvements in various metrics, including power consumption and active servers showing reductions of up to 55.16% and 35%, respectively. Furthermore, there has been a reduction in execution time of up to 67.74%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Cloud computing

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EARLY PREDICTION OF ALZHEIMER'S DISEASE USING MAGNETIC RESONANCE IMAGES (MRI) IN LEVERAGING DEEP LEARNING MODELS 2

EARLY PREDICTION OF ALZHEIMER'S DISEASE USING MAGNETIC RESON...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Kalpana, T. ThamilSelvan, R. Chitra, K. Rojaa, K.M. Santhiya, M. Sowmiyavarshan, A.D. Department of Computer Applications Kongu Engineering College Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (纸本)9781837242672

Alzheimer's disease is a well-known illness characterized by memory loss and cognitive decline. Since current treatments work best in the early stages, early detection is vital for effective management. The Magnetic Resonance Imaging (MRI) plays a key role in diagnosing Alzheimer's by providing detailed images of brain structures. Advances in deep learning have further improved MRI-based diagnosis through automated image ***2 and MobileNetV3 are lightweight convolutional neural networks (CNNs) known for their high performance and speed, making them ideal for resource-limited and real-time ***3 is a widely used CNN, known for its strong performance in image classification tasks. By training these deep learning models on complete MRI datasets, they offer the potential to aid doctors in making accurate and timely diagnoses. This approach couldlead to better outcomes and improved quality of life for *** research aims to enhance early detection and treatment methods for Alzheimer's disease, contributing to advancements in deep learning models. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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A Critical Analysis on Early Stage of Lungs and Colon Cancer Detection Using CNN Algorithm 1

A Critical Analysis on Early Stage of Lungs and Colon Cancer...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Ramesh, K. Bhavadharani, C. Narmatha, V.S. Samraj, Andrews Nandhakumar, R. Department of Computer Science and Applications Namakkal India Pg and Research Department of Computer Science and Applications Namakkal India Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350371185

One of the most dangerous problems that people experience globally is lung and colon tumor that has spread rapidly to become a normal medical challenging problem. It is essential to achieve an accurate and preliminary detection in order to minimize the hike of death. The complexity of the task entirely relies on the histopathologists experience. The analysis of the histopathology images of lung and colon cancer is crucial because early and accurate diagnosis of cancer histology is actually needed and since the treatment of cancer is aimed on the kind of histology, genetic profile and stage of the illness. Histologists may even endanger the patient's life. In recent times popularity of deep learning increase and recognized in the diagnosis and treatment of medical imaging. In order to identify lung and colon cancer using histopathological image and more effective augmentation techniques, this research involves studying and modifying the current CNN model. In the past few years, CNN has reported amazing progress in the fields of CT, MRI, and ultrasound. A novel technique that combines the advantages of two imaging modalities, 3D CT scan and histopathological image processing - to produce results that are more accurate and dependable. They are trained on the LC25000 dataset. CNN are the most preferred approach of deep learning algorithm for the advanced detection of lung and colon cancer. The level of accuracy offered by CNN in this regard may help to reduce the frequency and improve evaluation and quality © 2023 IEEE.

关键词： computerized tomography

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Deep Learning based Approach for Cassava Leaf Disease Detection 8

Deep Learning based Approach for Cassava Leaf Disease Detect...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Kaviya, S. Kiruthiga, M. Mahesh, C. Kongu Engineering College Department of Computer Applications Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350367904

Cassava is a vital staple crop in numerous tropical countries, to enhance plant disease detection using deep learning architecture including ResNet, EfficientNet, Incetion V3 and simple CNN. By levering these advance model significant improve accuracy compared to previous methods. This approach addresses the pressing issue of food insecurity by efficiently identifying diseases in crops. Types of Cassava disease are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB), along with the presence of Whitefly. The proposed work uses Convolution Neural Network (CNN) to identify the leaf diseases to quickly identify the specific disease that is affecting the crop. CNN can automatically detect important features without any human supervision. A vast collection of labelled cassava leaf photos comprising multiple disease classes is utilized for training the CNN model. To advance deep learning architectures, our research integrates techniques for data augmentation and transfer learning to further enchance model performance. © 2024 IEEE.

关键词： Plant diseases

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

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SN computer science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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